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business
forecasting predictive analytics
Forecasting And Predictive Analytics 7th Edition J. Holton Wilson, Barry Keating - Solutions
How do true cycles and the cycles typically found in business data differ?
How is the long-term trend determined for a time-series decomposition model?
What is the difference between seasonal factors and seasonal indices?
Discuss the trend, the seasonal, and the cyclical components.
Explain the similarity between how time-series decomposition and Winters’ exponential smoothing deal with seasonality.
Your company produces a favorite summertime food product, and you have been placed in charge of forecasting shipments of this product. The historical data below represent your company’s past experience with the
Develop an example to show how to set up a data file to apply regression analysis to combine forecasts.
Outline the process for combining forecast models explained in this appendix.
AmeriPlas, Inc., produces 20-ounce plastic drinking cups that are embossed with the names of prominent beers and soft drinks. The sales data
a. Construct a time-series graph of the sales data for HeathCo’s line of skiwear. Does there appear to be a seasonal pattern in the sales data? Explain why you think the results are as you have found.b. It seems logical that skiwear would sell better from October through March than from April
In Chapter 4, you worked with data on sales for a line of skiwear that is produced by HeathCo Industries. Barbara Lynch, product manager for the skiwear, has the responsibility of providing forecasts to top management of sales by quarter one year ahead. One of Ms. Lynch’s colleagues, Dick
The following inventory pattern has been observed in the Zahm Corporation over 12 months:MonthInventoryMonthInventoryApr-161,544Oct-161,208May-161,913Nov-162,467Jun-162,028Dec-162,101Jul-161,178Jan-171,662Aug-161,554Feb-172,432Sep-161,910Mar-172,443Use both three-month and five-month moving-average
Explain things that should be considered when selecting independent variables for a multiple regression model that will be used to make a forecast.
Describe some ways dummy variables can be useful in regression models.
Explain what is meant by a "dummy variable."
Describe how a regression plane differs from a regression line.
Explain the five-step process for evaluating a multiple regression model.
Explain the difference between bivariate (simple) regression and multiple regression.
Fifteen mid-western and mountain states have united in an effort to promote and forecast tourism. One aspect of their work has been related to the dollar amount spent per year on domestic travel (DTE) in each state. They have the following estimates for disposable personal income per capita (DPI)
Carolina Wood Products, Inc., a major manufacturer of household furniture, is interested in predicting expenditures on furniture (FURN) for the entire United States. It has the following data by quarter for 1998 through 2007:a. Prepare a naive forecast for 2017Q1 based on the following
Dick Staples, has mentioned to Barbara Lynch that he has found both the unemployment rate and the level of income to be useful predictors.a. Suppose that Ms. Lynch provides you with the following unemployment data for the northern region she is concerned with: b. Using Excel, plot a scatter
Barbara Lynch is the product manager for a line of skiwear produced by HeathCo Industries and privately branded for sale under several different names, including Northern Slopes and Jacque Monri. A new part of Ms. Lynch’s job is to provide a quarterly forecast of sales for the northern United
Mid-Valley Travel Agency (MVTA) has offices in 12 cities. The company believes that its monthly airline bookings are related to the mean income in those cities and has collected the following
Nelson Industries manufactures a part for a type of aircraft engine that is becoming obsolete. The sales history for the last 10 years is as follows:YearDec-08Dec-09Dec-10Dec-11Dec-12Dec-13Dec-14Dec-15Dec-16Dec-17Sales945875760690545420305285250210Time12345678910a). Plot sales versus time.b).
Explain what is meant by heteroscedasticity.
Explain the difference between the most common kind of correlation (the Pearson product moment correlation, discussed in Chapter 2) and serial correlation.
Explain the difference between a simple trend model and a causal model.
In this chapter, you learned four steps that should be used to evaluate a regression model. What is the first step and why is it so important? Explain the other three steps, indicating what you learn from each of those three steps.
How can seasonal data be forecast with a simple bivariate linear regression model? Explain the deseasonalize-forecast-reseasonalize process. How does the material in this chapter suggest that you find seasonal indices?
Why is it useful to look at data in a graph as well as in a table? What is the main advantage of seeing a graph of the data?For most people looking a long tables of numbers provides little useful insight. The following table is relatively short, but do you quickly see the error in the
What is an "event model?" Give some examples of when such a model might be useful.
What are some methods that might be useful to forecast "new products" for which there are few historical observations?
What data pattern would suggest the use of a Winters' exponential smoothing model?
When is a Holt's exponential smoothing model most appropriate?
For what type of data pattern would a simple exponential smoothing model be good as a forecast method?
Why is the term exponential used when describing exponential smoothing forecast models? negative slope
How are simple moving averages models different from exponential smoothing models?
Describe what is meant by the term moving average? When would a moving average be an appropriate forecast method?
Monthly data from March 2014 through September 2017 are provided below for the number of lunches served in public schools. You are charged with making a 12-month forecast of the meals to be served. Begin by plotting the data and examining it for the patterns of trend and seasonality.MonthMeals
The data in the table below are for retail sales in book stores by quarter.U.S. Retail Book Sales (in Millions of Dollars,
The data in the table below represent warehouse club and superstore sales in the eastern and central United States on a monthly basis. The data are in millions of dollars.
Plot the data presented in Exercise 7 to examine the possible existence of trend and seasonality in the data.Prepare three separate exponential smoothing models to forecast the full-service restaurant sales data using the monthly data.Prepare three separate exponential smoothing models to forecast
The number of service calls received at LaFortune Electric during four months is shown in the following table: Month…………..Number of Service
The number of tons of brake assemblies received at an auto parts distribution center last month was 670. The forecast tonnage was 720 for last month. The company uses a simple exponential smoothing model with a smoothing constant of 0.6 to develop its forecasts. What will be the company’s
Forecasters at Siegfried Corporation are using simple exponential smoothing to forecast the sales of its major product. They are trying to decide what smoothing constant will give the best results. They have tried a number of smoothing constants with the following results:Smoothing
Consider the following data on full-service restaurant sales. Calculate both the three-month and five-month moving averages for these data, and compare the forecasts by calculating the mean absolute percentage errors and MAPEs. The data are in millions of dollars.DateSales (000,000)DateSales
Consider the following rates offered on certificates of deposit at a large metropolitan bank during a recent year:Use a three-month average to forecast the rate for the following
Home sales are often considered an important determinant of the future health of the economy. Thus, there is widespread interest in being able to forecast home sales (HS). Quarterly data for HS are shown in the following table in thousands of units.DateHome sales (000) Per QuarterDateHome sales
Use exploratory data analysis to determine whether there is a trend and/or seasonality in mobile home shipments (MHS). The data by quarter are shown in the following
In a sample of 25 classes, the following numbers of students were observed.ClassNumber of studentsClassNumber of students1401437250153534216444201710529184063919367492020846212095222291045235811512451126425541343Calculate the mean, median, standard deviation, variance, and range for this
Twenty graduate students in business were asked how many credit hours they were taking in the current quarter. Their responses are shown as follows.Student NumberCredit HoursStudent NumberCredit Hours1211627125391394914135815106111667617988186912199101120101. Determine the mean, median, and mode
As the world’s economy becomes increasingly interdependent, various exchange rates between currencies have become important in making business decisions. For many U.S. businesses, the Japanese exchange rate (in yen per U.S. dollar) is an important decision variable. This exchange rate (EXRJ) is
CoastCo Insurance, Inc., is interested in developing a forecast of larceny thefts in the United States. It has found the following data:YearLarceny Thefts*YearLarceny Thefts*14,151107,19424,348117,14335,263126,71345,978136,59256,271146,92665,906157,25775,983167,50086,578177,70697,137187,872Plot
Suppose that you work for a major U.S. retail department store that has outlets nationwide. The store offers credit to customers in various forms, including store credit cards, and over the years has seen a substantial increase in credit purchases. The manager of credit sales is concerned about the
Go to the library and look up annual data for population in the United States from 2000 through the most recent year available. This series is available at a number of Internet sites, including http://www.economagic.com. Plot the actual data along with the forecast you would get by using the basic
Suppose that you work for a U.S. senator who is contemplating writing a bill that would put a national sales tax in place. Because the tax would be levied on the sales revenue of retail stores, the senator has asked you to prepare a forecast of retail store sales for year 8, based on data from year
In the chapter, you learned about many metrics that can be used to evaluate forecast accuracy. The MAPE was one of those that may be the most common in use. Explain what the MAPE tells a forecaster.
In this chapter, you saw an example of a naive forecast. Why do you think it is given that name? Describe how the naive forecast is developed.
The process of forecasting new products is difficult. Why? How can new products be forecast?
Explain how forecasting relates to having an efficient supply chain.
How does the organization of the material in this book relate to the stages of the evolution of prediction?
Describe the three phases of the evolution of forecasting/prediction.
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